Book description
This new text offers up-to-date coverage on the principles of digital communications, focusing on core principles and relating theory to practice.
Numerous examples, worked out in detail, have been included to help the student develop an intuitive grasp of the theory. The text also incorporates MATLAB-based computer experiments throughout, as well as themed examples and an abundance of homework problems.
Table of contents
- Coverpage
- Titlepage
- Copyright
- Dedication
- Preface
- Contents
- 1 Introduction
-
2 Fourier Analysis of Signals and Systems
- 2.1 Introduction
- 2.2 The Fourier Series
- 2.3 The Fourier Transform
- 2.4 The Inverse Relationship between Time-Domain and Frequency-Domain Representations
- 2.5 The Dirac Delta Function
- 2.6 Fourier Transforms of Periodic Signals
- 2.7 Transmission of Signals through Linear Time-Invariant Systems
- 2.8 Hilbert Transform
- 2.9 Pre-envelope
- 2.10 Complex Envelopes of Band-Pass Signals
- 2.11 Canonical Representation of Band-Pass Signals
- 2.12 Complex Low-Pass Representations of Band-Pass Systems
- 2.13 Putting the Complex Representations of Band-Pass Signals and Systems All Together
- 2.14 Linear Modulation Theory
- 2.15 Phase and Group Delays
- 2.16 Numerical Computation of the Fourier Transform
- 2.17 Summary and Discussion
-
3 Probability Theory and Bayesian Inference
- 3.1 Introduction
- 3.2 Set Theory
- 3.3 Probability Theory
- 3.4 Random Variables
- 3.5 Distribution Functions
- 3.6 The Concept of Expectation
- 3.7 Second-Order Statistical Averages
- 3.8 Characteristic Function
- 3.9 The Gaussian Distribution
- 3.10 The Central Limit Theorem
- 3.11 Bayesian Inference
- 3.12 Parameter Estimation
- 3.13 Hypothesis Testing
- 3.14 Composite Hypothesis Testing
- 3.15 Summary and Discussion
-
4 Stochastic Processes
- 4.1 Introduction
- 4.2 Mathematical Definition of a Stochastic Process
- 4.3 Two Classes of Stochastic Processes: Strictly Stationary and Weakly Stationary
- 4.4 Mean, Correlation, and Covariance Functions of Weakly Stationary Processes
- 4.5 Ergodic Processes
- 4.6 Transmission of a Weakly Stationary Process through a Linear Time-invariant Filter
- 4.7 Power Spectral Density of a Weakly Stationary Process
- 4.8 Another Definition of the Power Spectral Density
- 4.9 Cross-spectral Densities
- 4.10 The Poisson Process
- 4.11 The Gaussian Process
- 4.12 Noise
- 4.13 Narrowband Noise
- 4.14 Sine Wave Plus Narrowband Noise
- 4.15 Summary and Discussion
-
5 Information Theory
- 5.1 Introduction
- 5.2 Entropy
- 5.3 Source-coding Theorem
- 5.4 Lossless Data Compression Algorithms
- 5.5 Discrete Memoryless Channels
- 5.6 Mutual Information
- 5.7 Channel Capacity
- 5.8 Channel-coding Theorem
- 5.9 Differential Entropy and Mutual Information for Continuous Random Ensembles
- 5.10 Information Capacity Law
- 5.11 Implications of the Information Capacity Law
- 5.12 Information Capacity of Colored Noisy Channel
- 5.13 Rate Distortion Theory
- 5.14 Summary and Discussion
-
6 Conversion of Analog Waveforms into Coded Pulses
- 6.1 Introduction
- 6.2 Sampling Theory
- 6.3 Pulse-Amplitude Modulation
- 6.4 Quantization and its Statistical Characterization
- 6.5 Pulse-Code Modulation
- 6.6 Noise Considerations in PCM Systems
- 6.7 Prediction-Error Filtering for Redundancy Reduction
- 6.8 Differential Pulse-Code Modulation
- 6.9 Delta Modulation
- 6.10 Line Codes
- 6.11 Summary and Discussion
-
7 Signaling over AWGN Channels
- 7.1 Introduction
- 7.2 Geometric Representation of Signals
- 7.3 Conversion of the Continuous AWGN Channel into a Vector Channel
- 7.4 Optimum Receivers Using Coherent Detection
- 7.5 Probability of Error
- 7.6 Phase-Shift Keying Techniques Using Coherent Detection
- 7.7 M-ary Quadrature Amplitude Modulation
- 7.8 Frequency-Shift Keying Techniques Using Coherent Detection
- 7.9 Comparison of M-ary PSK and M-ary FSK from an Information-Theoretic Viewpoint
- 7.10 Detection of Signals with Unknown Phase
- 7.11 Noncoherent Orthogonal Modulation Techniques
- 7.12 Binary Frequency-Shift Keying Using Noncoherent Detection
- 7.13 Differential Phase-Shift Keying
- 7.14 BER Comparison of Signaling Schemes over AWGN Channels
- 7.15 Synchronization
- 7.16 Recursive Maximum Likelihood Estimation for Synchronization
- 7.17 Summary and Discussion
-
8 Signaling over Band-Limited Channels
- 8.1 Introduction
- 8.2 Error Rate Due to Channel Noise in a Matched-Filter Receiver
- 8.3 Intersymbol Interference
- 8.4 Signal Design for Zero ISI
- 8.5 Ideal Nyquist Pulse for Distortionless Baseband Data Transmission
- 8.6 Raised-Cosine Spectrum
- 8.7 Square-Root Raised-Cosine Spectrum
- 8.8 Post-Processing Techniques: The Eye Pattern
- 8.9 Adaptive Equalization
- 8.10 Broadband Backbone Data Network: Signaling over Multiple Baseband Channels
- 8.11 Digital Subscriber Lines
- 8.12 Capacity of AWGN Channel Revisited
- 8.13 Partitioning Continuous-Time Channel into a Set of Subchannels
- 8.14 Water-Filling Interpretation of the Constrained Optimization Problem
- 8.15 DMT System Using Discrete Fourier Transform
- 8.16 Summary and Discussion
-
9 Signaling over Fading Channels
- 9.1 Introduction
- 9.2 Propagation Effects
- 9.3 Jakes Model
- 9.4 Statistical Characterization of Wideband Wireless Channels
- 9.5 FIR Modeling of Doubly Spread Channels
- 9.6 Comparison of Modulation Schemes: Effects of Flat Fading
- 9.7 Diversity Techniques
- 9.8 “Space Diversity-on-Receive” Systems
- 9.9 “Space Diversity-on-Transmit” Systems
- 9.10 “Multiple-Input, Multiple-Output” Systems: Basic Considerations
- 9.11 MIMO Capacity for Channel Known at the Receiver
- 9.12 Orthogonal Frequency Division Multiplexing
- 9.13 Spread Spectrum Signals
- 9.14 Code-Division Multiple Access
- 9.15 The RAKE Receiver and Multipath Diversity
- 9.16 Summary and Discussion
-
10 Error-Control Coding
- 10.1 Introduction
- 10.2 Error Control Using Forward Error Correction
- 10.3 Discrete Memoryless Channels
- 10.4 Linear Block Codes
- 10.5 Cyclic Codes
- 10.6 Convolutional Codes
- 10.7 Optimum Decoding of Convolutional Codes
- 10.8 Maximum Likelihood Decoding of Convolutional Codes
- 10.9 Maximum a Posteriori Probability Decoding of Convolutional Codes
- 10.10 Illustrative Procedure for Map Decoding in the Log-Domain
- 10.11 New Generation of Probabilistic Compound Codes
- 10.12 Turbo Codes
- 10.13 EXIT Charts
- 10.14 Low-Density Parity-Check Codes
- 10.15 Trellis-Coded Modulation
- 10.16 Turbo Decoding of Serial Concatenated Codes
- 10.17 Summary and Discussion
- A Advanced Probabilistic Models
- B Bounds on the Q-Function
- C Bessel Functions
- D Method of Lagrange Multipliers
- E Information Capacity of MIMO Channels
- F Interleaving
- G The Peak-Power Reduction Problem in OFDM
- H Nonlinear Solid-State Power Amplifiers
- I Monte Carlo Integration
- J Maximal-Length Sequences
- K Mathematical Tables
- Glossary
- Bibliography
- Index
- Credits
Product information
- Title: Digital Communication Systems
- Author(s):
- Release date: February 2013
- Publisher(s): Wiley
- ISBN: 9780471647355
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